What really it takes to be Data Scientist?

You may have a great interest in Data Science and have questions like What is Data Science? What are the skills that I need to be a Data Scientist? What are all the things that a Data Scientist can do? Well in this blog let us have some idea about what Data Science does and the topmost skills that are required to be an inordinate Data Scientist. Let's get into it!


The data collected in a certain domain will be analyzed to extract meaningful insights that will be represented in a valuable way. With this, the companies will try to make conclusions on their future strategies. In addition to more Technical operations, it also gets involves with Mathematical operations too. The ultimate goal of Data Science is to help companies analyze and improve their current strategies!


Programming, Mathematics & Algorithmic and the domain knowledge in which you are going to work are the basic knowledge that you should have to be a “Data Scientist”. Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes create algorithms and predictive models to extract the data the business needs and help analyze the data and share insights with peers. The most common careers in data science include – Data scientist, Data analyst, Data Engineers, and Data architects. Data scientists are integral to supporting both leaders and developers in creating better products and paradigms.



To be a data scientist requires a lot of knowledge in the field that you are going to work. According to a survey 88% have at least a master's degree and 46% have a Ph.D., which gives us an inference that a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Apart from classes, trying to implement what you learned in your classes will help you to improve your skill as a Data Scientist.


Being open-source, R language is a good resource for Data Science. R is specifically designed for Data Science needs. As a Data Scientist, you can use R to solve any problems that you encounter in Data Science. But, if you have already mastered a programming language then it will be harder for you to get fit with the R language.

Many online platforms are available to learn it easily. R is used for data analysis in which it is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.


Python is the most common programming language that is required to be a Data Scientist along with Java, Perl, or C/C++. In a survey, 40 percent of the Data Scientists have told that Python is their major programming which shows us the major role of python in the field of Data Science.

It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can find any type of dataset you need on Google. Because of its versatility, you can use python for almost all the steps involved in data science processes.


Apache Hadoop is open-source software that facilitates a network of computers to solve problems that require massive datasets and computation power. Hadoop is highly scalable, that is designed to accommodate computation ranging from a single server to a cluster of thousands of machines. Even though Hadoop is written in java, you can program in Hadoop using multiple languages like python, C++, Perl, Ruby, etc.

Does Data scientists need Hadoop? The answer is a must but not compulsory. Even though Hadoop is not a necessary skill to be a Data Scientist, it is compulsory to make the process easier when it comes to analyzing a large amount of Data. The main functionality of Hadoop is the storage of Big Data. It also allows the users to store all forms of data, that is, both structured data and unstructured data. It also provides modules like Pig and Hive for the analysis of large-scale data.


The modern trend in Data Science is in the direction of NoSQL and using the Hadoop platform. But still, it is said that one can perform data science processes systematically using SQL itself. SQL is a programming language that helps you to carry out operations like add, delete and extract data from a database. It can also help you to carry out analytical functions and transform database structures.

As a Data Scientist, You need to be proficient in SQL. This is because SQL is designed to help you access, communicate and transfer data. We can get insights when we have a query on a database using SQL.


Apache Spark is becoming the most popular big data technology worldwide. It is a big data computation framework just like Hadoop. The only difference is that Spark is faster than Hadoop. This is because Hadoop reads and writes to disk, which makes it slower, but Spark caches its computations in memory.

Apache spark makes it possible for data scientists to prevent the loss of data in data science. The strength of Apache Spark lies in its speed and platform which makes it easy to carry out data science projects. With Apache spark, you can carry out the analytics from data intake to distributing computing.


A large number of data scientists are not proficient in machine learning areas and techniques. This includes neural networks, reinforcement learning, adversarial learning, etc. If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression, etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.


Artificial Intelligence and data science focuses on collecting, categorizing, strategizing, analyzing, and interpreting data. It is a specialized branch that deals with the development of data-driven solutions, data visualization tools, and techniques to analyze big data. With the help of machine learning algorithms, AI systems can automatically analyze data and uncover hidden trends, patterns, and insights that can be used by the industries to improve their efficiency.


In this modern world, we are getting tons of data every second, and as a Data Scientist to analyze and visualize these data you must be familiar with Data Visualization tools such as ggplot, d3.js, and Matplotlib, and Tableau. As we can understand data more clearly when it is represented in visual form maybe a graph or picture, it becomes important to know about these tools. The thing is, a lot of people do not understand serial correlation or p values. You need to show them visually what those terms represent in your results. Data visualization gives organizations the opportunity to work with data directly. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of competitions.


Examples of Unstructured Data are blogs like this, videos, customer reviews, social media posts, etc. (i.e,) Unstructured data are undefined content that does not fit into the database table. They are heavy texts lumped together. Sorting these types of data is difficult because they are not streamlined. Mostly unstructured data is referred to as ‘dark analytics” because of its complexity. Working with unstructured data helps you to unravel insights that can be useful for decision-making. As a data scientist, you must have the ability to understand and manipulate unstructured data from different platforms.

At last, we came across various tech skills that are needed to be a Data Scientist and now there are some soft skills too in which a Data Scientist should concentrate on.



Curiosity can be defined as the desire to acquire more knowledge. As a data scientist, you need to be able to ask questions about data because data scientists spend about 80 percent of their time discovering and preparing data. This is because the Data Science field is a field that is evolving very fast and you have to learn more to keep up with the pace. Curiosity is one of the skills you need to succeed as a data scientist. For example, initially, you may not see much insight in the data you have collected. Curiosity will enable you to sift through the data to find answers and more insights.


To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data. So understanding the things and having knowledge about the domain you are going to work is unavoidable in this field.


Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately. You also need to communicate by using data storytelling. As a data scientist, you have to know how to create a storyline around the data to make it easy for anyone to understand. For instance, presenting a table of data is not as effective as sharing the insights from those data in a storytelling format. Using storytelling will help you to properly communicate your findings to your employers.


A data scientist cannot work alone. You will have to work with company executives to develop strategies, work with product managers and designers to create better products, work with marketers to launch better-converting campaigns, work with client and server software developers to create data pipelines and improve workflow. You will literally have to work with everyone in the organization, including your customers.


Critical thinking is a valuable skill that easily transfers to any profession. For data scientists, it’s even more important because, in addition to finding insights, you need to be able to appropriately frame questions and understand how those results relate to the business or drive next steps that translate into action. It’s also important to objectively analyze problems when dealing with data interpretations before you form an opinion. Critical thinking in the field of data science means that you see all angles of a problem, consider the data source, and constantly stay curious.

We have covered almost all the technical and non-technical skills that a Data Scientist should master to shine in his career. Hope this info’s made your day and made you move a step ahead towards your career. Comment down your thoughts and to learn more about data science, check this out!

68 views0 comments